This application relates to image processing in digital cameras and other electronic digital image acquisition devices, and particularly to techniques of improving noise reduction techniques for such images.
Images obtained by digital cameras and other imaging systems contain random noise, which typically grows stronger as the ISO sensitivity grows higher. Noise reduction in digital cameras is becoming increasingly important and problematic because of several trends in the digital camera market which result in lower Signal to Noise Ratios (SNR), including the increasing of sensor resolution by reducing the pixel size and the providing better image quality at higher ISO sensitivities, which enables capture of images in low light conditions.
Prior art approaches typically effect noise reduction by either applying edge preserving filters on the image or suppressing chromatic components. Applying edge-preserving filters on the image, such as median filters, bilateral filters and others, are well known in the art. The difficulty encountered with these methods is that the size of the filter required for an effective noise reduction grows in proportion to the amount of noise in the image. However, the size of the filters is usually limited in order to save hardware costs, and software implementations tend to incur too much time and processing power to be practical. Suppressing the chromatic components of the pixels to zero in dark or gray areas reduces the chromatic component of the noise in these areas. The difficulty encountered using this method is that it affects only dark/gray areas, and it is also very likely to suppress real colors in the image. A seminal article on aspects of noise reduction in imagery and using sigma filters for this purpose is given in “Digital Image Smoothing and the Sigma Filter”, Lee, J. S., Computer Vision, Graphics, and Image Processing, 24, 255-269, 1983.
These various prior art methods tend to have a number of shortcomings when it comes to implementation in digital cameras, video, and other imaging systems. There will always be noise when an image is captured in low light conditions. The noise level will increase as the sensor pixel size is decreased due to sensor resolution issues and due to a trend to reduce sensor cost. Therefore, there is substantial room for improvements in digital imaging systems, even when considering future changes in the application environment.